{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 785,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the dependencies\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from statsmodels.tsa.arima_model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Storing the csvpaths\n",
    "csvpath_confirmed=\"time_series_19-covid-Confirmed.csv\"\n",
    "csvpath_deaths=\"time_series_19-covid-Deaths.csv\"\n",
    "csvpath_recovered=\"time_series_19-covid-Recovered.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading into a pandas DataFrame\n",
    "Confirmed_df1=pd.read_csv(csvpath_confirmed)\n",
    "Deaths_df1=pd.read_csv(csvpath_deaths)\n",
    "Recovered_df1=pd.read_csv(csvpath_recovered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Manipulating the Confirmed dataframe\n",
    "#Dropping the colums which are not required\n",
    "Confirmed_df1.drop(columns=[\"Province/State\",\"Lat\",\"Long\"],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>1/30/20</th>\n",
       "      <th>...</th>\n",
       "      <th>3/4/20</th>\n",
       "      <th>3/5/20</th>\n",
       "      <th>3/6/20</th>\n",
       "      <th>3/7/20</th>\n",
       "      <th>3/8/20</th>\n",
       "      <th>3/9/20</th>\n",
       "      <th>3/10/20</th>\n",
       "      <th>3/11/20</th>\n",
       "      <th>3/12/20</th>\n",
       "      <th>3/13/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>53</td>\n",
       "      <td>59</td>\n",
       "      <td>70</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>331</td>\n",
       "      <td>360</td>\n",
       "      <td>420</td>\n",
       "      <td>461</td>\n",
       "      <td>502</td>\n",
       "      <td>511</td>\n",
       "      <td>581</td>\n",
       "      <td>639</td>\n",
       "      <td>639</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>117</td>\n",
       "      <td>130</td>\n",
       "      <td>138</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>160</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>83</td>\n",
       "      <td>93</td>\n",
       "      <td>99</td>\n",
       "      <td>117</td>\n",
       "      <td>129</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country/Region  1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  \\\n",
       "0       Thailand        2        3        5        7        8        8   \n",
       "1          Japan        2        1        2        2        4        4   \n",
       "2      Singapore        0        1        3        3        4        5   \n",
       "3          Nepal        0        0        0        1        1        1   \n",
       "4       Malaysia        0        0        0        3        4        4   \n",
       "\n",
       "   1/28/20  1/29/20  1/30/20  ...  3/4/20  3/5/20  3/6/20  3/7/20  3/8/20  \\\n",
       "0       14       14       14  ...      43      47      48      50      50   \n",
       "1        7        7       11  ...     331     360     420     461     502   \n",
       "2        7        7       10  ...     110     117     130     138     150   \n",
       "3        1        1        1  ...       1       1       1       1       1   \n",
       "4        4        7        8  ...      50      50      83      93      99   \n",
       "\n",
       "   3/9/20  3/10/20  3/11/20  3/12/20  3/13/20  \n",
       "0      50       53       59       70       75  \n",
       "1     511      581      639      639      701  \n",
       "2     150      160      178      178      200  \n",
       "3       1        1        1        1        1  \n",
       "4     117      129      149      149      197  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filling NaN with a 0 for calculations\n",
    "Confirmed_df1=Confirmed_df1.fillna(0)\n",
    "Confirmed_df1.head()\n",
    "#len(Confirmed_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1/22/20</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  \\\n",
       "Country/Region                                                              \n",
       "Afghanistan                0        0        0        0        0        0   \n",
       "Albania                    0        0        0        0        0        0   \n",
       "Algeria                    0        0        0        0        0        0   \n",
       "Andorra                    0        0        0        0        0        0   \n",
       "Antigua and Barbuda        0        0        0        0        0        0   \n",
       "\n",
       "                     1/28/20  1/29/20  1/30/20  1/31/20  ...  3/4/20  3/5/20  \\\n",
       "Country/Region                                           ...                   \n",
       "Afghanistan                0        0        0        0  ...       1       1   \n",
       "Albania                    0        0        0        0  ...       0       0   \n",
       "Algeria                    0        0        0        0  ...      12      12   \n",
       "Andorra                    0        0        0        0  ...       1       1   \n",
       "Antigua and Barbuda        0        0        0        0  ...       0       0   \n",
       "\n",
       "                     3/6/20  3/7/20  3/8/20  3/9/20  3/10/20  3/11/20  \\\n",
       "Country/Region                                                          \n",
       "Afghanistan               1       1       4       4        5        7   \n",
       "Albania                   0       0       0       2       10       12   \n",
       "Algeria                  17      17      19      20       20       20   \n",
       "Andorra                   1       1       1       1        1        1   \n",
       "Antigua and Barbuda       0       0       0       0        0        0   \n",
       "\n",
       "                     3/12/20  3/13/20  \n",
       "Country/Region                         \n",
       "Afghanistan                7        7  \n",
       "Albania                   23       33  \n",
       "Algeria                   24       26  \n",
       "Andorra                    1        1  \n",
       "Antigua and Barbuda        0        1  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grouping the Confirmed cases dataframe by Country/Region\n",
    "Confirmed_group=Confirmed_df1.groupby([\"Country/Region\"])\n",
    "\n",
    "\n",
    "#Creating the final Confirmed dataframe to be used further\n",
    "Confirmed_final=Confirmed_group.sum()\n",
    "Confirmed_final.head()\n",
    "#len(Confirmed_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Manipulating the Deaths dataframe\n",
    "#Dropping the colums which are not required\n",
    "Deaths_df1.drop(columns=[\"Province/State\",\"Lat\",\"Long\"],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Country/Region</th>\n",
       "      <th>1/22/20</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Japan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country/Region  1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  \\\n",
       "0       Thailand        0        0        0        0        0        0   \n",
       "1          Japan        0        0        0        0        0        0   \n",
       "2      Singapore        0        0        0        0        0        0   \n",
       "3          Nepal        0        0        0        0        0        0   \n",
       "4       Malaysia        0        0        0        0        0        0   \n",
       "\n",
       "   1/28/20  1/29/20  1/30/20  ...  3/4/20  3/5/20  3/6/20  3/7/20  3/8/20  \\\n",
       "0        0        0        0  ...       1       1       1       1       1   \n",
       "1        0        0        0  ...       6       6       6       6       6   \n",
       "2        0        0        0  ...       0       0       0       0       0   \n",
       "3        0        0        0  ...       0       0       0       0       0   \n",
       "4        0        0        0  ...       0       0       0       0       0   \n",
       "\n",
       "   3/9/20  3/10/20  3/11/20  3/12/20  3/13/20  \n",
       "0       1        1        1        1        1  \n",
       "1      10       10       15       16       19  \n",
       "2       0        0        0        0        0  \n",
       "3       0        0        0        0        0  \n",
       "4       0        0        0        0        0  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filling NaN with a 0 for calculations\n",
    "Deaths_df1=Deaths_df1.fillna(0)\n",
    "Deaths_df1.head()\n",
    "#len(Confirmed_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1/22/20</th>\n",
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       "      <td>0</td>\n",
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       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Antigua and Barbuda</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  \\\n",
       "Country/Region                                                              \n",
       "Afghanistan                0        0        0        0        0        0   \n",
       "Albania                    0        0        0        0        0        0   \n",
       "Algeria                    0        0        0        0        0        0   \n",
       "Andorra                    0        0        0        0        0        0   \n",
       "Antigua and Barbuda        0        0        0        0        0        0   \n",
       "\n",
       "                     1/28/20  1/29/20  1/30/20  1/31/20  ...  3/4/20  3/5/20  \\\n",
       "Country/Region                                           ...                   \n",
       "Afghanistan                0        0        0        0  ...       0       0   \n",
       "Albania                    0        0        0        0  ...       0       0   \n",
       "Algeria                    0        0        0        0  ...       0       0   \n",
       "Andorra                    0        0        0        0  ...       0       0   \n",
       "Antigua and Barbuda        0        0        0        0  ...       0       0   \n",
       "\n",
       "                     3/6/20  3/7/20  3/8/20  3/9/20  3/10/20  3/11/20  \\\n",
       "Country/Region                                                          \n",
       "Afghanistan               0       0       0       0        0        0   \n",
       "Albania                   0       0       0       0        0        1   \n",
       "Algeria                   0       0       0       0        0        0   \n",
       "Andorra                   0       0       0       0        0        0   \n",
       "Antigua and Barbuda       0       0       0       0        0        0   \n",
       "\n",
       "                     3/12/20  3/13/20  \n",
       "Country/Region                         \n",
       "Afghanistan                0        0  \n",
       "Albania                    1        1  \n",
       "Algeria                    1        2  \n",
       "Andorra                    0        0  \n",
       "Antigua and Barbuda        0        0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grouping the Death cases dataframe by Country/Region\n",
    "Deaths_group=Deaths_df1.groupby([\"Country/Region\"])\n",
    "\n",
    "\n",
    "#Creating the final Death dataframe to be used further\n",
    "Deaths_final=Deaths_group.sum()\n",
    "Deaths_final.head()\n",
    "#len(Deaths_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Manipulating the Recovered dataframe\n",
    "#Dropping the colums which are not required\n",
    "Recovered_df1.drop(columns=[\"Province/State\",\"Lat\",\"Long\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>Japan</td>\n",
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       "      <td>46</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
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       "      <td>101</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
       "      <td>78</td>\n",
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       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>22</td>\n",
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       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country/Region  1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  \\\n",
       "0       Thailand        0        0        0        0        2        2   \n",
       "1          Japan        0        0        0        0        1        1   \n",
       "2      Singapore        0        0        0        0        0        0   \n",
       "3          Nepal        0        0        0        0        0        0   \n",
       "4       Malaysia        0        0        0        0        0        0   \n",
       "\n",
       "   1/28/20  1/29/20  1/30/20  ...  3/4/20  3/5/20  3/6/20  3/7/20  3/8/20  \\\n",
       "0        5        5        5  ...      31      31      31      31      31   \n",
       "1        1        1        1  ...      43      43      46      76      76   \n",
       "2        0        0        0  ...      78      78      78      78      78   \n",
       "3        0        0        0  ...       1       1       1       1       1   \n",
       "4        0        0        0  ...      22      22      22      23      24   \n",
       "\n",
       "   3/9/20  3/10/20  3/11/20  3/12/20  3/13/20  \n",
       "0      31       33       34       34       35  \n",
       "1      76      101      118      118      118  \n",
       "2      78       78       96       96       97  \n",
       "3       1        1        1        1        1  \n",
       "4      24       24       26       26       26  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filling NaN with a 0 for calculations\n",
    "Recovered_df1=Recovered_df1.fillna(0)\n",
    "Recovered_df1.head()\n",
    "#len(Recovered_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1/22/20</th>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>8</td>\n",
       "      <td>8</td>\n",
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       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  \\\n",
       "Country/Region                                                              \n",
       "Afghanistan                0        0        0        0        0        0   \n",
       "Albania                    0        0        0        0        0        0   \n",
       "Algeria                    0        0        0        0        0        0   \n",
       "Andorra                    0        0        0        0        0        0   \n",
       "Antigua and Barbuda        0        0        0        0        0        0   \n",
       "\n",
       "                     1/28/20  1/29/20  1/30/20  1/31/20  ...  3/4/20  3/5/20  \\\n",
       "Country/Region                                           ...                   \n",
       "Afghanistan                0        0        0        0  ...       0       0   \n",
       "Albania                    0        0        0        0  ...       0       0   \n",
       "Algeria                    0        0        0        0  ...       0       0   \n",
       "Andorra                    0        0        0        0  ...       0       0   \n",
       "Antigua and Barbuda        0        0        0        0  ...       0       0   \n",
       "\n",
       "                     3/6/20  3/7/20  3/8/20  3/9/20  3/10/20  3/11/20  \\\n",
       "Country/Region                                                          \n",
       "Afghanistan               0       0       0       0        0        0   \n",
       "Albania                   0       0       0       0        0        0   \n",
       "Algeria                   0       0       0       0        0        0   \n",
       "Andorra                   0       0       0       0        0        0   \n",
       "Antigua and Barbuda       0       0       0       0        0        0   \n",
       "\n",
       "                     3/12/20  3/13/20  \n",
       "Country/Region                         \n",
       "Afghanistan                0        0  \n",
       "Albania                    0        0  \n",
       "Algeria                    8        8  \n",
       "Andorra                    1        0  \n",
       "Antigua and Barbuda        0        0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grouping the Recovered cases dataframe by Country/Region\n",
    "Recovered_group=Recovered_df1.groupby([\"Country/Region\"])\n",
    "\n",
    "\n",
    "#Creating the final Recovered dataframe to be used further\n",
    "Recovered_final=Recovered_group.sum()\n",
    "Recovered_final.head()\n",
    "#len(Recovered_final)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "China_Predictive_Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>80860</td>\n",
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       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>80887</td>\n",
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       "      <td>49</td>\n",
       "      <td>80921</td>\n",
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       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>80932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>80945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    #Cumulative Confirmed Cases\n",
       "47                        80860\n",
       "48                        80887\n",
       "49                        80921\n",
       "50                        80932\n",
       "51                        80945"
      ]
     },
     "execution_count": 550,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_df=pd.DataFrame({\n",
    "                    \"#Cumulative Confirmed Cases\":Confirmed_final.loc[\"China\",:].tolist()\n",
    "                   })\n",
    "#US_df = US_df.set_index('Dates')\n",
    "china_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {},
   "outputs": [],
   "source": [
    "china_df_model = china_df\n",
    "#china_df_model.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   #Cumulative Confirmed Cases\n",
       "0                          NaN\n",
       "1                          NaN\n",
       "2                          NaN\n",
       "3                          NaN\n",
       "4                          NaN"
      ]
     },
     "execution_count": 552,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_df_model_mean = china_df_model.rolling(window = 7).mean()\n",
    "china_df_model_mean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 553,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ARIMA - Autoregressive (p) Integrated (d) Moving Average (q)\n",
    "#plot_acf is to identify parameter q\n",
    "#plot_pacf is to identify parameter p\n",
    "#ARIMA (p,d,q)\n",
    "\n",
    "plot_acf(china_df_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pacf(china_df_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_parameter_china = 52"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 631,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# p = 0,1  d = 0,1  q = 0,1,2,3,4,5\n",
    "#china_df_model.size >>> 52\n",
    "china_df_train = china_df_model[0:train_parameter_china]\n",
    "china_df_test = china_df_model[train_parameter_china:52]\n",
    "forecast_step_china = china_df_test.size\n",
    "forecast_step_china"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 847,
   "metadata": {},
   "outputs": [],
   "source": [
    "china_model = ARIMA(china_df_train, order=(1,0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 848,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:492: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "976.3933891667897"
      ]
     },
     "execution_count": 848,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_model_fit = china_model.fit()\n",
    "china_model_fit.aic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([80973.47364515, 81006.06012102, 81038.61875756, 81071.14957855,\n",
       "       81103.65260775, 81136.12786891, 81168.57538574, 81200.99518195,\n",
       "       81233.38728122, 81265.75170721, 81298.08848358, 81330.39763393,\n",
       "       81362.67918186, 81394.93315097])"
      ]
     },
     "execution_count": 634,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_forecast = china_model_fit.forecast(steps = 14)[0]\n",
    "china_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.sqrt(mean_squared_error(china_df_test,china_forecast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {},
   "outputs": [],
   "source": [
    "#china_df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Predictive Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>80973.473645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>81006.060121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>81038.618758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>81071.149579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>81103.652608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Predictive Cases\n",
       "0                  80973.473645\n",
       "1                  81006.060121\n",
       "2                  81038.618758\n",
       "3                  81071.149579\n",
       "4                  81103.652608"
      ]
     },
     "execution_count": 640,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_predict_df= pd.DataFrame({\"#Cumulative Predictive Cases\":china_forecast.tolist()})\n",
    "china_predict_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "      <th>#Cumulative Predictive Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>80945.0</td>\n",
       "      <td>80945.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80973.473645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81006.060121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81038.618758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81071.149579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81103.652608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81136.127869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81168.575386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81200.995182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81233.387281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81265.751707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81298.088484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81330.397634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81362.679182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81394.933151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    #Cumulative Confirmed Cases  #Cumulative Predictive Cases\n",
       "51                      80945.0                  80945.000000\n",
       "52                          NaN                  80973.473645\n",
       "53                          NaN                  81006.060121\n",
       "54                          NaN                  81038.618758\n",
       "55                          NaN                  81071.149579\n",
       "56                          NaN                  81103.652608\n",
       "57                          NaN                  81136.127869\n",
       "58                          NaN                  81168.575386\n",
       "59                          NaN                  81200.995182\n",
       "60                          NaN                  81233.387281\n",
       "61                          NaN                  81265.751707\n",
       "62                          NaN                  81298.088484\n",
       "63                          NaN                  81330.397634\n",
       "64                          NaN                  81362.679182\n",
       "65                          NaN                  81394.933151"
      ]
     },
     "execution_count": 642,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_prelim_predict_df = pd.concat([china_df, china_predict_df],sort=False, ignore_index=True)\n",
    "china_prelim_predict_df.loc[[51],'#Cumulative Predictive Cases'] = 80945.0\n",
    "china_prelim_predict_df.tail(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 643,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(china_prelim_predict_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 791,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "      <th>#Cumulative Predictive Cases</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>643.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>920.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1406.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2075.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Confirmed Cases  #Cumulative Predictive Cases       date\n",
       "0                        548.0                           NaN 2020-01-22\n",
       "1                        643.0                           NaN 2020-01-23\n",
       "2                        920.0                           NaN 2020-01-24\n",
       "3                       1406.0                           NaN 2020-01-25\n",
       "4                       2075.0                           NaN 2020-01-26"
      ]
     },
     "execution_count": 791,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_prelim_predict_df['date'] = pd.date_range(start='1/22/2020',periods=len(china_prelim_predict_df),freq='D')\n",
    "china_prelim_predict_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "x_axis = np.arange(1, len(china_prelim_predict_df)+1)\n",
    "tick_locations_china = []\n",
    "for x in x_axis:\n",
    "    tick_locations_china.append(x)\n",
    "    \n",
    "plt.title(\"Cumulative Covid-19 cases in China \")\n",
    "plt.xlabel(\"Period in Days\")\n",
    "plt.ylabel(\"# of cases\")\n",
    "plt.grid()\n",
    "plt.xlim(0,len(china_prelim_predict_df)-1)\n",
    "    \n",
    "confirmed_cases_china, = plt.plot(x_axis, china_prelim_predict_df.loc[:,'#Cumulative Confirmed Cases'], color=\"blue\", label=\"Cumulative Confirmed Cases\")\n",
    "arima_cases_china, = plt.plot(x_axis, china_prelim_predict_df.loc[:,'#Cumulative Predictive Cases'], color ='red', label = 'Arima Predictive Model')\n",
    "\n",
    "plt.legend(handles=[confirmed_cases_china, arima_cases_china], loc=\"upper left\")\n",
    "plt.xticks(tick_locations_china)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "US_Predictive_Modelling_With_China_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>2179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    #Cumulative Confirmed Cases\n",
       "47                          583\n",
       "48                          959\n",
       "49                         1281\n",
       "50                         1663\n",
       "51                         2179"
      ]
     },
     "execution_count": 645,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_df=pd.DataFrame({\n",
    "                    \"#Cumulative Confirmed Cases\":Confirmed_final.loc[\"US\",:].tolist()\n",
    "                   })\n",
    "#US_df = US_df.set_index('Dates')\n",
    "US_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "metadata": {},
   "outputs": [],
   "source": [
    "US_df_model = US_df\n",
    "#US_df_model.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Confirmed Cases\n",
       "0                          NaN\n",
       "1                          NaN\n",
       "2                          NaN\n",
       "3                          NaN\n",
       "4                          NaN"
      ]
     },
     "execution_count": 647,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_df_model_mean = US_df_model.rolling(window = 7).mean()\n",
    "US_df_model_mean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 648,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ARIMA - Autoregressive (p) Integrated (d) Moving Average (q)\n",
    "#plot_acf is to identify parameter q\n",
    "#plot_pacf is to identify parameter p\n",
    "#ARIMA (p,d,q)\n",
    "\n",
    "plot_acf(US_df_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 649,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pacf(US_df_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 849,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>5509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Confirmed Cases\n",
       "0                          548\n",
       "1                          643\n",
       "2                          920\n",
       "3                         1406\n",
       "4                         2075\n",
       "5                         2877\n",
       "6                         5509"
      ]
     },
     "execution_count": 849,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_df.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 836,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Confirmed Cases\n",
       "0                            1\n",
       "1                            1\n",
       "2                            2\n",
       "3                            2\n",
       "4                            5"
      ]
     },
     "execution_count": 836,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_parameter_US = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 655,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# p = 0,1  d = 0,1  q = 0,1,2\n",
    "#US_df_model.size >>> 52\n",
    "US_df_train = china_df_model[0:train_parameter_US]\n",
    "US_df_test = US_df_model[train_parameter_US:20]\n",
    "forecast_step_US = US_df_test.size\n",
    "forecast_step_US"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 841,
   "metadata": {},
   "outputs": [],
   "source": [
    "US_model = ARIMA(US_df_train, order=(1,2,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 842,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.8508458686848"
      ]
     },
     "execution_count": 842,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_model_fit = US_model.fit()\n",
    "US_model_fit.aic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3852.26619562,  5003.44298022,  6330.70401849,  7834.06071546,\n",
       "        9513.51382015, 11369.06338174, 13400.70940345, 15608.45188551,\n",
       "       17992.29082793, 20552.22623071, 23288.25809384, 26200.38641733,\n",
       "       29288.61120117, 32552.93244538])"
      ]
     },
     "execution_count": 711,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_forecast = US_model_fit.forecast(steps = forecast_step_US)[0]\n",
    "US_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18268.231720408734"
      ]
     },
     "execution_count": 712,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(US_df_test,US_forecast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    #Cumulative Confirmed Cases\n",
       "6                             5\n",
       "7                             5\n",
       "8                             5\n",
       "9                             7\n",
       "10                            8"
      ]
     },
     "execution_count": 713,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Predictive Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3852.266196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5003.442980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6330.704018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7834.060715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>9513.513820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Predictive Cases\n",
       "0                   3852.266196\n",
       "1                   5003.442980\n",
       "2                   6330.704018\n",
       "3                   7834.060715\n",
       "4                   9513.513820"
      ]
     },
     "execution_count": 714,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_predict_df= pd.DataFrame({\"#Cumulative Predictive Cases\":US_forecast.tolist()})\n",
    "US_predict_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 715,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "      <th>#Cumulative Predictive Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Confirmed Cases  #Cumulative Predictive Cases\n",
       "0                          1.0                           NaN\n",
       "1                          1.0                           NaN\n",
       "2                          2.0                           NaN\n",
       "3                          2.0                           NaN\n",
       "4                          5.0                           NaN"
      ]
     },
     "execution_count": 715,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_prelim_predict_df = pd.concat([US_df, US_predict_df],sort=False, ignore_index=True)\n",
    "US_prelim_predict_df.loc[[51],'#Cumulative Predictive Cases'] = 2179.0\n",
    "US_prelim_predict_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 716,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#US_df_model_mean.iloc[0:52,:]\n",
    "len(US_prelim_predict_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "      <th>#Cumulative Predictive Cases</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Confirmed Cases  #Cumulative Predictive Cases       date\n",
       "0                          1.0                           NaN 2020-01-22\n",
       "1                          1.0                           NaN 2020-01-23\n",
       "2                          2.0                           NaN 2020-01-24\n",
       "3                          2.0                           NaN 2020-01-25\n",
       "4                          5.0                           NaN 2020-01-26"
      ]
     },
     "execution_count": 790,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_prelim_predict_df['date'] = pd.date_range(start='1/22/2020',periods=len(US_prelim_predict_df),freq='D')\n",
    "US_prelim_predict_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "x_axis = np.arange(1, len(US_prelim_predict_df)+1)\n",
    "tick_locations_US = []\n",
    "for x in x_axis:\n",
    "    tick_locations_US.append(x)\n",
    "    \n",
    "plt.title(\"Cumulative Covid-19 cases in US & China \")\n",
    "plt.xlabel(\"Period in Days\")\n",
    "plt.ylabel(\"# of cases\")\n",
    "plt.grid()\n",
    "plt.xlim(0,len(US_prelim_predict_df)-1)\n",
    "    \n",
    "confirmed_cases_US, = plt.plot(x_axis, US_prelim_predict_df.loc[:,'#Cumulative Confirmed Cases'], color=\"blue\", label=\"Cumulative Confirmed Cases - US\")\n",
    "arima_cases_US, = plt.plot(x_axis, US_prelim_predict_df.loc[:,'#Cumulative Predictive Cases'], color ='red', label = 'Arima Predictive Model - US')\n",
    "confirmed_cases_china, = plt.plot(x_axis, china_prelim_predict_df.loc[:,'#Cumulative Confirmed Cases'], color=\"green\", label=\"Cumulative Confirmed Cases - China\")\n",
    "arima_cases_china, = plt.plot(x_axis, china_prelim_predict_df.loc[:,'#Cumulative Predictive Cases'], color ='red', label = 'Arima Predictive Model - China')\n",
    "\n",
    "plt.legend(handles=[confirmed_cases_US, arima_cases_US,confirmed_cases_china,arima_cases_china], loc=\"upper left\")\n",
    "plt.xticks(tick_locations_US)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Canada_Predictive_Modelling_With_China_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    #Cumulative Confirmed Cases\n",
       "47                           77\n",
       "48                           79\n",
       "49                          108\n",
       "50                          117\n",
       "51                          193"
      ]
     },
     "execution_count": 718,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canada_df=pd.DataFrame({\n",
    "                    \"#Cumulative Confirmed Cases\":Confirmed_final.loc[\"Canada\",:].tolist()\n",
    "                   })\n",
    "#canada_df = US_df.set_index('Dates')\n",
    "canada_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "metadata": {},
   "outputs": [],
   "source": [
    "canada_df_model = canada_df\n",
    "#canada_df_model.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Confirmed Cases\n",
       "0                          NaN\n",
       "1                          NaN\n",
       "2                          NaN\n",
       "3                          NaN\n",
       "4                          NaN"
      ]
     },
     "execution_count": 720,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canada_df_model_mean = canada_df_model.rolling(window = 7).mean()\n",
    "canada_df_model_mean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 721,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gc+zYe4ihGQj6Yk1Nou3VHcx94Tucf9oSh72ZAdmM0ukF1hRNrwZ2FU0vAM4BHlThNuwnA5skXRUR3RnUX1MGR/L84JXDDIz4h1JmVluyCPxHgHWSTgdeAq4BfnZsYUTsB5aPTUt6EPhovYV9Pgl69w2we/+QbyJiZjWp4sCPiJykm4D7gGbgjojYJuk2oDsiNlVaR63rOzjMi68NTOvJ2JnmX+ua1Z9MfngVEZuBzSXzbh2n7MVZ1FkLBkZy/OCVwxwYrK/uG/9a16w++eJpJyCXT3j+lcM82bu/7sIejv61bnD0r3XNbPZy4E/R3oNDPNHbX9d99f61rll98rV0JunQcI7nXznMwaH6O6Iv5V/rmtUnH+Efx2g+YWffIZ56aX9DhD3417pm9cqBP4E9B4Z4oqefPQeG67b7phz/WtesPjnwy8gnweHhHDv7Dmd6JcvZxL/WNas/7sMvMppPePG1AQ77doIV8zh+s9rjwKdwNcs9B4bp2TdQ8R2nzOP4zWpVw3fpHBga5Xsv7ecHrxx22GfE4/jNalPDBv5ILmHH3oNse+kAh4dP/IbhdiyP4zerTQ3ZpTOaT9ja008+8RH9dPA4frPa1JBH+EmEw34aeRy/WW1qyMC36eVx/Ga1yYFv0yKLcfxJEjz2wj6+9lgvj72wj8Tfyswq0pB9+Fb7PLTTLHs+wrea5KGdZtnLJPAlXS7pWUk7JN1cZvmvSnpa0pOStkg6LYt6rX55aKdZ9ioOfEnNwO3AFcB64FpJ60uKPQ50RcQbgb8F/rDSeq2+jQ3tLOahnWaVyeII/wJgR0TsjIgR4KvA1cUFIuKfI2IgnXwIWJ1BvVbHPLTTLHtZBP4qoKdoujedN54bgG+WWyDpRkndkrr7+voyaJrNVh7aaZa9LAK/3P/AsuPnJP1XoAv4dLnlEbExIroioquzszODptlsVunQTg/rNDtaFsMye4E1RdOrgV2lhSRdCtwC/GREDGdQr9m4PKzT7FhZHOE/AqyTdLqkNuAaYFNxAUnnAX8BXBURezOo02xCHtZpdqyKAz8icsBNwH3AM8C9EbFN0m2SrkqLfRroAP5G0lZJm8Z5O7NMeFin2bEy+aVtRGwGNpfMu7Xo9aVZ1GM2WVldsdN37rJ64ksrWF0aG9a57cVXoLmF9taWKQ/r9HkAqze+tILVpSyGdfo8gNUbB77VrUqHdfo8gNUbd+mYjSOL8wA+B2C1xIFvNo5KzwP4HIDVGnfpmI2j0vMAPgdgtcaBbzaBSs4D+ByA1Rp36ZhNE/8WwGqNA99smvi3AFZr3KVjNk38WwCrNQ58s2lUC78F8GWibYy7dMxqWKXnAdwlZMV8hG9Wwyq91aO7hKyYA9+shlV6HsBDQ62YA9+sxlVyHmCsS6jYiQ4N9XmA2c99+GZ1zENDrZiP8M3qmIeGWrFMAl/S5ZKelbRD0s1llrdLuidd/rCktVnUa2bH56GhNqbiLh1JzcDtwGVAL/CIpE0R8XRRsRuAfRHxeknXAH8A/EyldZvZ9PPQ0PqhiMr2tJIuAj4ZEe9Ipz8GEBG/X1TmvrTMdyW1AC8DnTFB5UtPOysu+/gdJ9SmrU9sBWDDuRvKLo8IDg7nxl1/+9NPAbBu/TknVH8W71Ht9d0Gt2FMRPDia4MMDI8CQk1ibmszpy6di3T8wD44lOOl/kGK/7dLsGrxXBbMmdwxZ0RwaDjP0GieOa3NdLQ3T6ru2WrhnNYTXvfeD/34oxHRVW5ZFoH/HuDyiPhAOv0+4MKIuKmozFNpmd50+rm0zCsl73UjcCNAxyln/NiVv3V3RW0bz/EC38yONha4w6N52qcYuH0Hh3nl0Mgx8zs72li+oH1SdVeywyl+n+/v2AnNbaxcecoJ7TRmauc9XYGfxSidclusdC8ymTJExEZgI0BXV1fc84sXVd66MoZzeR57wSeczGbCYy/s47Pf3n5Ul1B7SxPX//jpnH/akkmvjwqnHCMgnwQ//cZVk1offtitlMxZDM0t9B0cZtHcqXUrJUnwi/d9gXzHSbzzR99+wlct/aUvfwyAW39j07hlLjx96Ql3d937ofGXZXHSthdYUzS9Gtg1Xpm0S2cR8FoGdZtZjRsbGtre0oRgyr8WzuKk8dhII1raQE1THmk0tsM4tP5dDJ7+Fj777e2FHcgUTz4nSTCy7PUMnvbmqpy8zuII/xFgnaTTgZeAa4CfLSmzCbgO+C7wHuDbE/Xfm1n9GBsaeqLX9M/ivgIT7TQm8y3hqB0GRw9Nneq3jEPr3wXNLXz229tn/OR1xUf4EZEDbgLuA54B7o2IbZJuk3RVWuwLwDJJO4BfBY4Zumlm9aupSZx/2hLeff7qKQ8NrfQbAlT+i+Na+JaRhUx+aRsRm4HNJfNuLXo9BPyXLOoys8ZS6TcE+OFOo3Ro6GR3GrXwLSMLvrSCmdW8sW8IJxqMle40Kt1hQHa3vKyEA9/MGkIlO41a+JaRBQe+mdkkVPtbRhYc+GZmM6TSnUbF9VelVjMzm3EOfDOzBuHANzNrEA58M7MG4cA3M2sQDnwzswbhwDczaxAOfDOzBuHANzNrEA58M7MG4cA3M2sQDnwzswZRUeBLWirpAUnb0+djrggkaYOk70raJulJST9TSZ1mZnZiKj3CvxnYEhHrgC2Uv3XhAPD+iDgbuBz4jKSZuwC0mZkBlQf+1cBd6eu7gHeVFoiI70fE9vT1LmAv0FlhvWZmNkWVBv5JEbEbIH1eMVFhSRcAbcBz4yy/UVK3pO6+vr4Km2ZmZsWOewMUSd8CTi6z6JapVCTpFOBu4LqISMqViYiNwEaArq6umMr7m5nZxI4b+BFx6XjLJO2RdEpE7E4Dfe845RYC/wR8IiIeOuHWmpnZCau0S2cTcF36+jrgH0oLSGoD/h74YkT8TYX1mZnZCao08D8FXCZpO3BZOo2kLkmfT8u8F3grcL2kreljQ4X1mpnZFFV0E/OIeBW4pMz8buAD6eu/Bv66knrMzKxy/qWtmVmDcOCbmTUIB76ZWYNoyMBva27i9OXzaWlWtZtiZjZjGjLwJXHyojlsWLOYFQvbq90cM7MZ0ZCBP6a1uYkzOjs4Z9VCOtorGrBkZlbzGjrwxyyY08o5qxZyRud8Wt3NY2Z1yoe1KUmsWDiHJfPb6HltgL0HhwlfzcfM6oiP8Eu0Njfxus4OfnTVIhbM8f7QzOqHA38c89tbOGfVIs5YMZ+2FnfzmNns50PY41ixYA5L57XRu2+Qlw8MuZvHzGYtH+FPQktzE2uXz+eNqxexcK73kWY2Oznwp2BeWwtnr1zEupM6aGvxpjOz2cWHqydgeUc7S+a18dK+QXbtH3Q3j5nNCj5MPUHNTeLUZfM4d/ViFs1trXZzzMyOy4FfobltzaxfuZA3nNRBe6s3p5nVrooSStJSSQ9I2p4+L5mg7EJJL0n6XCV11qplHe2cu3oxq5fMpcmjOM2sBlV6SHozsCUi1gFb0unx/Dbwfyusr6Y1N4k1S+dx7prFLJnvbh4zqy2VBv7VwF3p67uAd5UrJOnHgJOA+yusb1aY09rMj5y8kHNWLXT/vpnVjEoD/6SI2A2QPq8oLSCpCfgj4NeP92aSbpTULam7r6+vwqZV34I5raxfuZD1Kxf6Mg1mVnXHTSFJ3wJOLrPolknW8WFgc0T0SBN3bkfERmAjQFdXV90Mdlw0t5VFqxaxf2CUnn0DHBzKVbtJZtaAjhv4EXHpeMsk7ZF0SkTslnQKsLdMsYuAt0j6MNABtEk6FBET9ffXpUXzWlk0bxH7Do/Qu2+QQ8MOfjObOZX2M2wCrgM+lT7/Q2mBiPi5sdeSrge6GjHsiy2Z38aS+W28dniEntcGGBjJV7tJZtYAKu3D/xRwmaTtwGXpNJK6JH2+0sbVu6Xz2zh3zWLWndTB3LbmajfHzOpcRUf4EfEqcEmZ+d3AB8rMvxO4s5I669HyjnaWzW/jlUMj9O4bYGg0qXaTzKwOeehIjZBE54J2lne00XdwmN7+QYYd/GaWIQd+jRm71eLyjnb6B0fpHxihf3DU4W9mFXPg16imJrF0fhtL57cBMDCSY99AYQdwcCjnK3Sa2ZQ58GeJeW0tzGtrYdXiueTyCfsHR9k3MMr+wRFGck5/Mzs+B/4s1NLcxLKOdpZ1tANwaDhX6PoZGOXQsI/+zaw8B34d6GhvoaO9hdVLYDSf0J8e+fcPjDKad/qbWYEDv860NjfRuaCdzgWFo/+h0TyHhnMcHs6lz3nyiXcCZo3IgV/n5rQ2M6e1meVp909EMDSacHB4lMPDeQ6nOwPvA8zqnwO/wUhibltz4Ze9CwrzIoKBkfyRbwGHhnMMjOR9LsCszjjwDUnMb29hfnvLketbJ0lweCTH4Gie4dGE4VyeofTZo4LMZicHvpXV1CQWzGllwZxjb+CSJMFwLmFoNM9w7uidwXAuIecTxWY1yYFvU9bUVNQtVMZoPinsCNIdwmg+YTQf5JKE0VwwmhR2Cj55bDazHPiWudbmJlqbm+hon/jjlU+C0XxCLgly+YSRfGFHkMtH4XW6YxjNJyRRmO99hNmJc+Bb1TQ3ieamqV0WOiLIJYVvB/kkyEeQz6fPydGPXBIk6fwkgghIorDTKEynrxPvSKwxOPBtVpFEa7NonYbbByTpjuGHO4T0mR9OUzQvjjyXvKbwHjG2TtH8wvPYvB+uk84pWV60TtEOKdIS5UZRTabcRPu2GGdoVj3tD6dn9FllbzpTI+Ic+GappibRxMT3XTabzSq645WkpZIekLQ9fV4yTrlTJd0v6RlJT0taW0m9ZmY2dZXe4vBmYEtErAO2pNPlfBH4dEScBVxA+Zudm5nZNKo08K8G7kpf3wW8q7SApPVAS0Q8ABARhyJioMJ6zcxsiioN/JMiYjdA+ryiTJk3AP2SvibpcUmfluQ7dpuZzbDjnrSV9C3g5DKLbplCHW8BzgNeBO4Brge+UKauG4EbAU499dRJvr2ZmU3GcQM/Ii4db5mkPZJOiYjdkk6hfN98L/B4ROxM1/k68CbKBH5EbAQ2AnR1ddXTSDAzs6qrtEtnE3Bd+vo64B/KlHkEWCKpM51+G/B0hfWamdkUVRr4nwIuk7QduCydRlKXpM8DREQe+CiwRdL3AAF/WWG9ZmY2RRX98CoiXgUuKTO/G/hA0fQDwBsrqcvMzCqj8X5KXW2S+oAXKniL5cArGTVnuriN2XAbs+E2Zqea7TwtIjrLLajZwK+UpO6I6Kp2OybiNmbDbcyG25idWm1npX34ZmY2SzjwzcwaRD0H/sZqN2AS3MZsuI3ZcBuzU5PtrNs+fDMzO1o9H+GbmVkRB76ZWYOY1YEv6XJJz0raIemYa/FLapd0T7r84WrceEXSGkn/nN78ZZuk/16mzMWS9kvamj5urUI7n5f0vbT+7jLLJemz6bZ8UtL5M9y+M4u2z1ZJByR9pKTMjG9HSXdI2ivpqaJ5k70x0HVpme2SritXZhrb+GlJ/5H+W/69pMXjrDvh52Ka2/hJSS8V/XteOc66E+bADLTznqI2Pi9p6zjrzsi2nFCkN3OebQ+gGXgOeB3QBjwBrC8p82Hgz9PX1wD3VKGdpwDnp68XAN8v086LgW9UeXs+DyyfYPmVwDcpXBrjTcDDVf63f5nCD0yquh2BtwLnA08VzftD4Ob09c3AH5RZbymwM31ekr5eMoNtfDuF+1QA/EG5Nk7mczHNbfwk8NFJfBYmzIHpbmfJ8j8Cbq3mtpzoMZuP8C8AdkTEzogYAb5K4YYsxYpv0PK3wCWSZvSmpRGxOyIeS18fBJ4BVs1kGzJyNfDFKHgIWJxeIbUaLgGei4hKfomdiYj4F+C1ktnHvTEQ8A7ggYh4LSL2AQ8Al89UGyPi/ojIpZMPAauno+7JGmc7TsZkciAzE7UzzZb3Al+ZrvorNZsDfxXQUzTdy7FBeqRM+uHeDyybkdaVkXYpnQc8XGbxRZKekPRNSWfPaMMKArhf0qPpfQlKTWZ7z5RrGP8/VbW3I0zuxkC1tD1/gcK3t3KO97mYbjel3U53jNM1Vkvb8S3AnojYPs7yam/LWR345Y7US8eYTqbMjJDUAfwd8JGIOFCy+DEK3RPnAn8CfH2m22QrGZ4AAAJNSURBVAe8OSLOB64AfknSW0uW18S2lNQGXAX8TZnFtbAdJ6tWtuctQA740jhFjve5mE5/BpwBbAB2U+guKVUT2zF1LRMf3VdzWwKzO/B7gTVF06uBXeOVkdQCLOLEvjZWRFIrhbD/UkR8rXR5RByIiEPp681Aq6TlM9nGiNiVPu8F/p7CV+Vik9neM+EK4LGI2FO6oBa2Y2rPWHeXJr4xUFW3Z3qi+J3Az0XayVxqEp+LaRMReyIiHxEJhUuql6u76tsRjuTLuync0a+sam7LMbM58B8B1kk6PT3qu4bCDVmKFd+g5T3At8f7YE+XtF/vC8AzEfG/xylz8ti5BUkXUPh3eXUG2zhf0oKx1xRO6D1VUmwT8P50tM6bgP1j3RYzbNyjqGpvxyKTuTHQfcDbJS1Juyrens6bEZIuB34TuCoiBsYpM5nPxXS2sfgc0X8ep+7J5MBMuBT4j4joLbew2tvyiGqeMa70QWHkyPcpnKW/JZ13G4UPMcAcCl/9dwD/DryuCm38CQpfMZ8EtqaPK4EPAR9Ky9wEbKMwwuAh4MdnuI2vS+t+Im3H2LYsbqOA29Nt/T2gqwrbch6FAF9UNK+q25HCzmc3MErhaPMGCueJtgDb0+eladku4PNF6/5C+tncAfz8DLdxB4W+77HP5NhotpXA5ok+FzPYxrvTz9qTFEL8lNI2ptPH5MBMtjOdf+fY57CobFW25UQPX1rBzKxBzOYuHTMzmwIHvplZg3Dgm5k1CAe+mVmDcOCbmTUIB76ZWYNw4JuZNYj/D8I7TuA3HdUNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ARIMA - Autoregressive (p) Integrated (d) Moving Average (q)\n",
    "#plot_acf is to identify parameter q\n",
    "#plot_pacf is to identify parameter p\n",
    "#ARIMA (p,d,q)\n",
    "\n",
    "plot_acf(canada_df_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 722,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ARIMA - Autoregressive (p) Integrated (d) Moving Average (q)\n",
    "#plot_acf is to identify parameter q\n",
    "#plot_pacf is to identify parameter p\n",
    "#ARIMA (p,d,q)\n",
    "\n",
    "plot_pacf(canada_df_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 838,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>80860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>80887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>80921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>80932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>80945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    #Cumulative Confirmed Cases\n",
       "47                        80860\n",
       "48                        80887\n",
       "49                        80921\n",
       "50                        80932\n",
       "51                        80945"
      ]
     },
     "execution_count": 838,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 837,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Confirmed Cases\n",
       "0                            0\n",
       "1                            0\n",
       "2                            0\n",
       "3                            0\n",
       "4                            1"
      ]
     },
     "execution_count": 837,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canada_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 738,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_parameter_canada = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 740,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# p = 0,1,2  d = 0,1  q = 0,1,2,3\n",
    "#canada_df_model.size >>> 52\n",
    "canada_df_train = china_df[0:train_parameter_canada]\n",
    "canada_df_test = canada_df_model[train_parameter_canada:18]\n",
    "forecast_step_canada = canada_df_test.size\n",
    "forecast_step_canada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 852,
   "metadata": {},
   "outputs": [],
   "source": [
    "canada_model = ARIMA(canada_df_train, order=(1,1,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 853,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.955352728349965"
      ]
     },
     "execution_count": 853,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canada_model_fit = canada_model.fit()\n",
    "canada_model_fit.aic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1691.35842464, 1977.35242989, 2263.34442177, 2549.33642004,\n",
       "       2835.32841828, 3121.32041653, 3407.31241477, 3693.30441302,\n",
       "       3979.29641126, 4265.28840951, 4551.28040775, 4837.272406  ,\n",
       "       5123.26440424, 5409.25640249])"
      ]
     },
     "execution_count": 754,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canada_forecast = canada_model_fit.forecast(steps = 14)[0]\n",
    "canada_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3728.715205973237"
      ]
     },
     "execution_count": 755,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(canada_df_test,canada_forecast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "metadata": {},
   "outputs": [],
   "source": [
    "#canada_df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Predictive Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1691.358425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1977.352430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2263.344422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2549.336420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2835.328418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Predictive Cases\n",
       "0                   1691.358425\n",
       "1                   1977.352430\n",
       "2                   2263.344422\n",
       "3                   2549.336420\n",
       "4                   2835.328418"
      ]
     },
     "execution_count": 757,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canada_predict_df= pd.DataFrame({\"#Cumulative Predictive Cases\":canada_forecast.tolist()})\n",
    "canada_predict_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 758,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "      <th>#Cumulative Predictive Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Confirmed Cases  #Cumulative Predictive Cases\n",
       "0                          0.0                           NaN\n",
       "1                          0.0                           NaN\n",
       "2                          0.0                           NaN\n",
       "3                          0.0                           NaN\n",
       "4                          1.0                           NaN"
      ]
     },
     "execution_count": 758,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canada_prelim_predict_df = pd.concat([canada_df, canada_predict_df],sort=False, ignore_index=True)\n",
    "canada_prelim_predict_df.loc[[51],'#Cumulative Predictive Cases'] = 193.0\n",
    "canada_prelim_predict_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 759,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#canada_df_model_mean.iloc[0:52,:]\n",
    "len(canada_prelim_predict_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 815,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Cumulative Confirmed Cases</th>\n",
       "      <th>#Cumulative Predictive Cases</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   #Cumulative Confirmed Cases  #Cumulative Predictive Cases       date\n",
       "0                          0.0                           NaN 2020-01-22\n",
       "1                          0.0                           NaN 2020-01-23\n",
       "2                          0.0                           NaN 2020-01-24\n",
       "3                          0.0                           NaN 2020-01-25\n",
       "4                          1.0                           NaN 2020-01-26"
      ]
     },
     "execution_count": 815,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canada_prelim_predict_df['date'] = pd.date_range(start='1/22/2020',periods=len(canada_prelim_predict_df),freq=None)\n",
    "canada_prelim_predict_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 816,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "x_axis = np.arange(1, len(US_prelim_predict_df)+1)\n",
    "tick_locations_US = []\n",
    "for x in x_axis:\n",
    "    tick_locations_US.append(x)\n",
    "    \n",
    "plt.title(\"Modelling of Covid-19 cases in China & US & Canada \")\n",
    "plt.xlabel(\"Period in Days\")\n",
    "plt.ylabel(\"# of cases\")\n",
    "plt.grid()\n",
    "plt.xlim(0,len(US_prelim_predict_df)-1)\n",
    "    \n",
    "confirmed_cases_US, = plt.plot(x_axis, US_prelim_predict_df.loc[:,'#Cumulative Confirmed Cases'], color=\"blue\", label=\"Cumulative Confirmed Cases - US\")\n",
    "arima_cases_US, = plt.plot(x_axis, US_prelim_predict_df.loc[:,'#Cumulative Predictive Cases'], color ='red', label = 'Arima Predictive Model - US')\n",
    "\n",
    "confirmed_cases_china, = plt.plot(x_axis, china_prelim_predict_df.loc[:,'#Cumulative Confirmed Cases'], color=\"green\", label=\"Cumulative Confirmed Cases - China\")\n",
    "arima_cases_china, = plt.plot(x_axis, china_prelim_predict_df.loc[:,'#Cumulative Predictive Cases'], color ='red', label = 'Arima Predictive Model - China')\n",
    "\n",
    "confirmed_cases_canada, = plt.plot(x_axis, canada_prelim_predict_df.loc[:,'#Cumulative Confirmed Cases'], color=\"black\", label=\"Cumulative Confirmed Cases - Canada\")\n",
    "arima_cases_canada, = plt.plot(x_axis, canada_prelim_predict_df.loc[:,'#Cumulative Predictive Cases'], color ='red', label = 'Arima Predictive Model - Canada')\n",
    "\n",
    "plt.legend(handles=[confirmed_cases_US, arima_cases_US,confirmed_cases_china,arima_cases_china,confirmed_cases_canada,arima_cases_canada], loc=\"upper left\")\n",
    "plt.xticks(tick_locations_US)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 834,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "x_axis = np.arange(1, len(US_prelim_predict_df)+1)\n",
    "tick_locations_US = []\n",
    "for x in x_axis:\n",
    "    tick_locations_US.append(x)\n",
    "    \n",
    "plt.title(\"Modelling of Covid-19 cases in China & US & Canada \")\n",
    "plt.xlabel(\"Period in Days\")\n",
    "plt.ylabel(\"# of cases\")\n",
    "plt.grid()\n",
    "plt.xlim(1,len(US_prelim_predict_df))\n",
    "    \n",
    "confirmed_cases_US, = plt.plot(x_axis, US_prelim_predict_df.loc[:,'#Cumulative Confirmed Cases'], color=\"blue\", label=\"Cumulative Confirmed Cases - US\")\n",
    "arima_cases_US, = plt.plot(x_axis, US_prelim_predict_df.loc[:,'#Cumulative Predictive Cases'], color ='red', label = 'Arima Predictive Model - US')\n",
    "\n",
    "confirmed_cases_china, = plt.plot(x_axis, china_prelim_predict_df.loc[:,'#Cumulative Confirmed Cases'], color=\"green\", label=\"Cumulative Confirmed Cases - China\")\n",
    "arima_cases_china, = plt.plot(x_axis, china_prelim_predict_df.loc[:,'#Cumulative Predictive Cases'], color ='red', label = 'Arima Predictive Model - China')\n",
    "\n",
    "confirmed_cases_canada, = plt.plot(x_axis, canada_prelim_predict_df.loc[:,'#Cumulative Confirmed Cases'], color=\"black\", label=\"Cumulative Confirmed Cases - Canada\")\n",
    "arima_cases_canada, = plt.plot(x_axis, canada_prelim_predict_df.loc[:,'#Cumulative Predictive Cases'], color ='red', label = 'Arima Predictive Model - Canada')\n",
    "\n",
    "plt.legend(handles=[confirmed_cases_US, arima_cases_US,confirmed_cases_china,arima_cases_china,confirmed_cases_canada,arima_cases_canada], loc=\"upper left\")\n",
    "plt.xticks(x_axis,US_prelim_predict_df['date'].dt.strftime('%m-%d-%y'),rotation ='vertical')\n",
    "plt.axvline(x=52, color='k', linestyle='--')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
